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CWI_README.txt

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-CWI datasets for English, German, and Spanish
-==============================================
-
-Distributed by the LT group at Universität Hamburg, Germany, October 2017.
-contact email: yimam@informatik.uni-hamburg.de
-web: http://lt.informatik.uni-hamburg.de
-
-
-Introduction
-------------
-
-Complex word Identification (CWI) is a sub-task of lexical simplification (LS), which identifies difficult words or phrases in a text. There are very few CWI datasets available, and mostly limited to English language. To alleviate this problem, we have collected CWI datasets for English, German, and Spanish.
-
-Data set collection procedures
-------------------------------
-
-We collected complex word and phrase annotations (sequences of words, up to maximum 50 characters) using the Amazon Mechanical Turk (MTurk) crowdsourcing platform, from native and non-native English, German, and Spanish speakers.  We collect annotations using MTurk, on a paragraph-level HIT (Human Intelligence Task), which is 5-10 sentences long.
-
-The English datasets consists of three genres:
-
-* Professionally written news
-* News written by amateurs (WikiNews)
-* Wikipedia articles
-
-The German and Spanish datasets are compiled from German Wikipedia and Spanish Wikipedia articles.
-
-Files
------
-The zip file contains this README file and three sub folders, namely English, German, and Spanish.
-Under each sub folder, there are both training and development annotation files.
-The test files are currently held back, as we are preparing a shared task on multilingual CWI.
-
-For English, there are the following files:
-
-## development and training dataset the news genre, with annotations
-English_News_Train.tsv (6515 lines, 1500943 bytes)
-English_News_Dev.tsv (824 lines  186306 bytes)
-
-## development and training dataset for news genre, all the sentences in a HIT with out the actual annotations
-English_News_HITs_Train.tsv (180lines, 153004 bytes)
-English_News_HITs_Dev.tsv (23lines, 19225 bytes)
-
-## development and training dataset the Wikinews genre, with annotations
-English_WikiNews_Train.tsv (99 lines, 89136 bytes)
-English_WikiNews_Dev.tsv (476 lines, 102916 bytes)
-
-## development and training dataset for Wikinews genre, all the sentences in a HIT with out the actual annotations
-English_WikiNews_HITs_Train.tsv (3878 lines, 864816 bytes)
-English_WikiNews_HITs_Dev.tsv (13 lines, 11525 bytes)
-
-## development and training dataset the Wikipedia genre, with annotations
-English_Wikipedia_Train.tsv (2903 lines, 673747 bytes)
-English_Wikipedia_Dev.tsv (369 lines, 79832 bytes)
-
-## development and training dataset for Wikipedia genre, all the sentences in a HIT with out the actual annotations
-English_Wikipedia_HITs_Train.tsv (81 lines, 63281 bytes)
-English_Wikipedia_HITs_Dev.tsv (10 lines, 8014 bytes)
-
-For German, there are the following files
-## development and training dataset, with annotations
-German_Train.tsv (2629 lines, 618781 bytes)
-German_Dev.tsv (325 lines, 70478 bytes)
-
-## development and training dataset, all the sentences in a HIT with out the actual annotations
-German_HITs_Train.tsv (85 lines, 91609 bytes)
-German_HITs_Dev.tsv (11 lines, 11937 bytes)
-
-For Spanish, there are the following files
-## development and training dataset, with annotations
-Spanish_Train.tsv (5601 lines, 2954656 bytes)
-Spanish_Dev.tsv (701 lines, 312785 bytes)
-
-## development and training dataset, all the sentences in a HIT with out the actual annotations
-Spanish_HITs_Train.tsv (141 lines, 166885 bytes)
-Spanish_HITs_Dev.tsv (18 lines, 19161 bytes)
-
-
-Data formats
-------------
-
-These datasets contain information about complex phrases annotated with some statistics. Each line represents a sentence with one complex phrase (CP) annotation and relevant information, each separated by a TAB character.
-
-* First column shows the HIT ID of the sentence. All sentences with the same ID belong to the same HIT.
-* Second column shows the actual sentence where there exists a complex phrase annotation.
-* The third and fourth columns display the start and end offsets of the complex phrase annotation in this sentence.
-* The fifth column represents the actual complex phrase annotation.
-* The sixth, seventh, and eighth columns show the number of native annotators, the number of non-native annotators and the total number of annotators who have marked this complex phrase.
-
-Examples
---------
-
-ID1     Both China and the Philippines flexed their muscles on Wednesday.      31      37      flexed                            2      7    9
-
-ID1     Both China and the Philippines flexed their muscles on Wednesday.      31       51     flexed their muscles   4    2   6
-
-Here, we can see that the phrase "flexed" is marked as complex phrase by 2 native and 7 non-native English speakers where as the phrase "flexed their muscles" is marked by 4 native and 2 non native English speakers.
-
-Download
---------
-
-The datasets are available from
-https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/complex-word-identification-dataset.html
-
-License
--------
-The data is distributed under CC-BY 4.0 license, see https://creativecommons.org/licenses/by/4.0/ for details
-
-Publications
-------------
-Please cite one of these publications, if you use the data in your research:
-
-* Seid Muhie Yimam, Sanja Štajner, Martin Riedl, and Chris Biemann (2017): CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups. In Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017). Taipei, Taiwan [For English data in different genres]
-* Seid Muhie Yimam, Sanja Štajner, Martin Riedl, and Chris Biemann (2017): Multilingual and Cross-Lingual Complex Word Identification. In Proceedings of The 2017 International Conference on Recent Advances in Natural Language Processing (RANLP). Varna, Bulgaria [for multilingual data]